Abstract
Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the "AquaBOT"). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L-1along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. The AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.
| Original language | English |
|---|---|
| Pages (from-to) | 425-435 |
| Number of pages | 11 |
| Journal | ACS ES and T Water |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 11 2022 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). Acknowledgments This material is based on work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. The authors thank E. Henson for assistance with AquaBOT development and testing, A. Kadlec, P. Penningroth, M. Smith, S. Stoller, and T. Vroman for assistance with field sampling, and B. Belden, K. Larsen, T. Puls, J. Swanson, L. Tesdell, and the Southfork Watershed Alliance for assistance with site access. The authors greatly appreciate comments provided by S.C. Brooks and three anonymous reviewers who improved this manuscript. Data are archived on the BioenergyKDF ( http://bioenergykdf.net ).
Keywords
- drone
- environmental monitoring
- lotic ecosystems
- nitrate concentration
- point-source inputs
- spatial variation
- turbidity